vllm.transformers_utils.utils ¶

def _maybe_json_dict(path: str | PathLike) -> dict[str, str]:
    with open(path) as f:
        try:
            return json.loads(f.read())
        except Exception:
            return dict[str, str]()

def _maybe_space_split_dict(path: str | PathLike) -> dict[str, str]:
    parsed_dict = dict[str, str]()
    with open(path) as f:
        for line in f.readlines():
            try:
                model_name, redirect_name = line.strip().split()
                parsed_dict[model_name] = redirect_name
            except Exception:
                pass
    return parsed_dict

@cache
def check_gguf_file(model: str | PathLike) -> bool:
    """Check if the file is a GGUF model."""
    model = Path(model)
    if not model.is_file():
        return False
    elif model.suffix == ".gguf":
        return True

    try:
        with model.open("rb") as f:
            header = f.read(4)

        return header == b"GGUF"
    except Exception as e:
        logger.debug("Error reading file %s: %s", model, e)
        return False

def convert_model_repo_to_path(model_repo: str) -> str:
    """When VLLM_USE_MODELSCOPE is True convert a model
    repository string to a Path str."""
    if not envs.VLLM_USE_MODELSCOPE or Path(model_repo).exists():
        return model_repo
    from modelscope.utils.file_utils import get_model_cache_root

    return os.path.join(get_model_cache_root(), model_repo)

def is_cloud_storage(model_or_path: str) -> bool:
    return is_s3(model_or_path) or is_gcs(model_or_path)

def is_gcs(model_or_path: str) -> bool:
    return model_or_path.lower().startswith("gs://")

def is_gguf(model: str | Path) -> bool:
    """Check if the model is a GGUF model.

    Args:
        model: Model name, path, or Path object to check.

    Returns:
        True if the model is a GGUF model, False otherwise.
    """
    model = str(model)

    # Check if it's a local GGUF file
    if check_gguf_file(model):
        return True

    # Check if it's a remote GGUF model (repo_id:quant_type format)
    return is_remote_gguf(model)

@cache
def is_remote_gguf(model: str | Path) -> bool:
    """Check if the model is a remote GGUF model."""
    model = str(model)
    return (
        (not is_cloud_storage(model))
        and (not model.startswith(("http://", "https://")))
        and ("/" in model and ":" in model)
        and is_valid_gguf_quant_type(model.rsplit(":", 1)[1])
    )

def is_s3(model_or_path: str) -> bool:
    return model_or_path.lower().startswith("s3://")

def is_valid_gguf_quant_type(gguf_quant_type: str) -> bool:
    """Check if the quant type is a valid GGUF quant type."""
    return getattr(GGMLQuantizationType, gguf_quant_type, None) is not None

@cache
def maybe_model_redirect(model: str) -> str:
    """
    Use model_redirect to redirect the model name to a local folder.

    :param model: hf model name
    :return: maybe redirect to a local folder
    """

    model_redirect_path = envs.VLLM_MODEL_REDIRECT_PATH

    if not model_redirect_path:
        return model

    if not Path(model_redirect_path).exists():
        return model

    redirect_dict = _maybe_json_dict(model_redirect_path) or _maybe_space_split_dict(
        model_redirect_path
    )
    if redirect_model := redirect_dict.get(model):
        logger.info("model redirect: [ %s ] -> [ %s ]", model, redirect_model)
        return redirect_model

    return model

def modelscope_list_repo_files(
    repo_id: str,
    revision: str | None = None,
    token: str | bool | None = None,
) -> list[str]:
    """List files in a modelscope repo."""
    from modelscope.hub.api import HubApi

    api = HubApi()
    api.login(token)
    # same as huggingface_hub.list_repo_files
    files = [
        file["Path"]
        for file in api.get_model_files(
            model_id=repo_id, revision=revision, recursive=True
        )
        if file["Type"] == "blob"
    ]
    return files

def parse_safetensors_file_metadata(path: str | PathLike) -> dict[str, Any]:
    with open(path, "rb") as f:
        length_of_metadata = struct.unpack("<Q", f.read(8))[0]
        metadata = json.loads(f.read(length_of_metadata).decode("utf-8"))
        return metadata

def split_remote_gguf(model: str | Path) -> tuple[str, str]:
    """Split the model into repo_id and quant type."""
    model = str(model)
    if is_remote_gguf(model):
        parts = model.rsplit(":", 1)
        return (parts[0], parts[1])
    raise ValueError(
        "Wrong GGUF model or invalid GGUF quant type: %s.\n"
        "- It should be in repo_id:quant_type format.\n"
        "- Valid GGMLQuantizationType values: %s",
        model,
        GGMLQuantizationType._member_names_,
    )